ch 6:

Question 1

1. APPROACHES TO FORECASTING
   There are two general approaches to forecasting: qualitative and quantitative.
   Qualitative methods consist mainly of subjective inputs, which often defy precise numerical
   description. Quantitative methods involve either the projection of historical data or the
   development of associative models that attempt to utilize causal (explanatory) variables to make
   a forecast.

Question 2

Quantitative techniques consist mainly of analyzing objective, or hard data. They
usually avoid personal biases that sometimes contaminate qualitative methods. In practice, either
approach or a combination of both approaches might be used to develop a forecast.

Question 2

Qualitative techniques permit inclusion of soft information (e.g., human factors,
personal opinions, hunches) in the forecasting process. Those factors are often omitted or
downplayed when quantitative techniques are used because they are difficult or impossible to
quantify.

Question 2

2. FORECASTING TECHNIQUES

Answer 2

2.1.Forecasts Based on Judgement and Opinion
2.2.Forecast Based on Time-Series Data

Question 2

2.2.Forecast Based on Time-Series Data

Answer 2

a. Executive Opinions
b. Salesforce Opinions
c. Consumer Surveys

Question 3

Members of the sales staff or the customer service staff are often good sources of information because of their direct contact with consumers. They are often aware of any plans the customers may be considering for the future. There are, however, several drawbacks to using salesforce opinions. One is that staff members may be unable to distinguish between what customers would like to do and what they actually will do. Another is that these people are sometimes overly influenced by recent experiences.

Answer 3

b. Salesforce Opinions

Question 3

A small group of upper-level managers (e.g., in marketing, operations, and finance) may meet and collectively develop a forecast. This approach is often used as a part of long-range planning and new product development. It has the advantage of bringing together the considerable knowledge and talents of various managers. However, there is the risk that the view of one person will prevail, and the possibility that diffusing responsibility for the forecast over the entire group may result in less pressure to produce a good forecast.

Answer 3

a. Executive Opinions

Question 4

Because it is the consumers who ultimately determine demand, it seems natural to solicit input from them. In some instances, every customer or potential customer can be contacted. However, usually there are too many customers or there is no way to identify all potential customers. The obvious advantage of consumer surveys is that they can tap information that might not be available elsewhere. Surveys can be expensive and time- consuming. In addition, even under the best conditions, surveys of the general public must contend with the possibility of irrational behavior patterns.

Answer 4

c. Consumer Surveys

Question 5

is a time-ordered sequence of observations taken at regular intervals (e.g., hourly, daily, weekly, monthly, quarterly, annually). The data may be measurements of demand, earnings, profits, shipments, accidents, output, precipitation, productivity, or the consumer price index. Forecasting techniques based on time-series data are made on the assumption that future values of the series can be estimated from past values. Although no attempt is made to identify variables that influence the series, these methods are widely used, often with quite satisfactory results.

Answer 5

A time series

Question 6

Analysis of time-series data requires the analyst to identify the underlying behavior of the series. This can often be accomplished by merely plotting the data and visually examining the plot. One or more patterns might appear: trends, seasonal variations, cycles, or variations around an average. In addition, there will be random and perhaps irregular variations. These behaviors can be described as follows:

Answer 6

a. Trend refers to a long-term upward or downward movement in the data.
b. Seasonality refers to short-term, fairly regular variations generally related to factors such as the calendar or time of day.
c. Cycles are wavelike variations of more than one year’s duration. These are often related to a variety of economic, political, and even agricultural conditions. d. Irregular variations are due to unusual circumstances such as severe weather conditions, strikes, or a major change in a product or service. e. Random variations are residual variations that remain after all other behaviors have been accounted for.

Question 7

refers to a long-term upward or downward movement in the data.

Answer 7

a. Trend

Question 8

refers to short-term, fairly regular variations generally related to factors such as the calendar or time of day.

Answer 8

b. Seasonality

Question 9

are wavelike variations of more than one year’s duration. These are often related to a variety of economic, political, and even agricultural conditions.

Answer 9

c. Cycles

Question 10

are due to unusual circumstances such as severe weather conditions, strikes, or a major change in a product or service.

Answer 10

d. Irregular variations

Question 11

are residual variations that remain after all other behaviors have been accounted for.

Answer 11

e. Random variations

Question 12

It is a simple but widely used approach to forecasting is the naive approach. A naive forecast uses a single previous value of a time series as the basis of a forecast. The naive approach can be used with a stable series (variations around an average), with seasonal variations, or with trend. Although at first glance the naive approach may appear too simplistic, it is nonetheless a legitimate forecasting tool. Consider the advantages: It has virtually no cost, it is quick and easy to prepare because data analysis is non-existent, and it is easily understandable. The main objection to this method is its inability to provide highly accurate forecasts.

Answer 12

2.2.1. Naïve Methods

Question 13

Averaging techniques smooth variations in the data. Ideally, it would be desirable to completely remove any randomness from the data and leave only “real” variations, such as changes in the demand. As a practical matter, however, it is usually impossible to distinguish between these two kinds of variations, so the best one can hope for is that the small variations are random and the large variations are “real.”

Answer 13

2.2.2. Techniques for Averaging

Question 14

One weakness of the naive method is that the forecast just traces the actual data, with a lag of one period; it does not smooth at all. But by expanding the amount of historical data a forecast is based on, this difficulty can be overcome. A moving average forecast uses a number of the most recent actual data values in generating a forecast. The moving average forecast can be computed using the following equation:

Answer 14

2.2.2.1.Moving Average

Question 14

Averaging techniques smooth fluctuations in a time series because the individual highs and lows in the data offset each other when they are combined into an average. A forecast based on an average thus tends to exhibit less variability than the original data. Three techniques for averaging are described as follows;

Answer 14

2.2.2. Techniques for Averaging

Question 15

A weighted moving average is similar to a moving average, except that it typically assigns more weight to the most recent values in a time series.

Answer 15

2.2.2.2.Weighted Moving Average

Question 16

Exponential smoothing is a sophisticated weighted averaging method that is still relatively easy to use and understand. Each new forecast is based on the previous forecast plus a percentage of the difference between that forecast and the actual value of the series at that point.

Answer 16

2.2.2.3.Exponential Smoothing